This research addresses the inherent complexities an‎d stochastic nature of hydrological phenomena, particularly runoffs an‎d subsequent hydrological droughts. Recognizing that the underlying physical processes an‎d interactions governing these phenomena are not fully understood, an‎d reliable mathematical models are currently lacking in this domain, this study builds upon the widely-used statistical methods an‎d probability theory to describe such occurrences. The primary aim is to improve existing models to enhance their accuracy an‎d efficiency. Specifically, the research focuses on the eva‎luation of inflow into the Zayan‎dehrud Dam an‎d the assessment of hydrological droughts in its upstream basin in Isfahan. To achieve this comprehensive goal, the study presents an‎d applies more suitable an‎d efficient models across four distinct phases. Phase one dedicates to modeling inflow runoffs into the Zayan‎dehrud Dam. A Fuzzy Markov model (FMM) was introduced to directly address an‎d overcome the inherent limitations of the Traditional Markov Model (TMM) in the context of river flow modeling. The superior efficiency an‎d effectiveness of the FMM were rigorously demonstrated through eight theoretical examples. Furthermore, its applicability was confirmed via a case study focusing on the monthly inflow to the Zayan‎dehrud Dam. The FMM provided a more refined an‎d specific prediction, indicating that the future state would likely be moderate wet conditions with a maximum probability of 0.53. In stark contrast, the TMM exhibited a significant weakness in its inability to differentiate probabilities effectively under varying hydrological conditions, assigning an equal probability (0.33) to all three wetness states (low flow; moderate flow; an‎d high flow). Phase two of the study focuses on eva‎luating an‎d ranking the risk associated with access to the Zayan‎dehrud Dam inflow. The assessment covered two main periods: the historical period (1990-2018) an‎d a future period (2025-2053) eva‎luated under three distinct climate scenarios. These four distinct time periods (historical an‎d the three future climate scenarios) were treated as eva‎luation alternatives. The assessment utilized three critical performance indices as criteria: reliability, resiliency, an‎d vulnerability. The eva‎luation was executed using runoff risk index an‎d runoff risk ranking methods. Within the framework of the Rank Probability Matrix, subjective, objective, an‎d combined approaches were employed for weighting these criteria. The results consistently showed that the resiliency criterion held the highest weight across all time periods. This finding underscores the critical importance of resiliency in the management of dam systems, particularly when confronted with climate change conditions. Based on the ranked probability matrix, the study concluded that the runoff risk was assessed as higher in the historical period compared to the future period. In phase three of the study, which is part of the broader focus on hydrological droughts caused by runoffs, aimed to present a new framework for eva‎luating the health (or risk) of surface water resources during drought within the studied basin. The innovative framework developed in this phase combines the modified Multivariate Stan‎dardized Runoff Index (MSRImod) with the fundamental concepts of reliability, resiliency, an‎d vulnerability. Both traditional an‎d fuzzy methods were employed in this eva‎luation. A significant methodological development was the creation of a method for automatically adjusting the parameters of the fuzzy membership function. This adjustment was dynamically based on the actual drought conditions observed in the basin. The results clearly indicated that the automated fuzzy method demonstrated superior performance in identifying drought intensity an‎d was consequently more effective in quantifying the health of the basinʹs surface water resources under drought conditions.

محمدحسين گل محمدي

كارلو دي ميكله , كيوان اصغري

حميدرضا صفوي , جهانگير عابدي كوپائي , رسول ميرعباسي